H045074150

Ms. G. Jyothi et al Int. Journal of Engineering Research and Applications www.ijera.com
ISSN : 2248-9622, Vol. 4, Issue 5( Version 7), May 2014, pp.41-50
www.ijera.com 41 | P a g e
Fuzzy Expert Model for Evaluation of Faculty Performance in
Technical Educational Institutions
Ms. G. Jyothi*, Mrs. Ch. Parvathi**, Mr. P. Srinivas***, and Mr. Sk. Althaf
Rahaman****
*(Department of Information Technology, Vignan’s Institute of Information Technology: Visakhapatnam-
530049, AP, INDIA)
** (Department of Information Technology, Vignan’s Institute of Information Technology: Visakhapatnam-
530049, AP, INDIA)
*** (Department of Information Technology, Vignan’s Institute of Information Technology: Visakhapatnam-
530049, AP, INDIA)
** ** (Department of Information Technology, Vignan’s Institute of Information Technology: Visakhapatnam-
530049, AP, INDIA)
ABSTRACT
In developing countries, higher education is seen as an essential means for creation and development of
resources and for improving the life of people to whom it has to serve. Worldwide National policies on higher
education are been given increasing importance to improve the quality of education on offer. Consequently, the
evaluation of Faculty performance in teaching activity is especially relevant for the academic institutions. It
helps to define efficient plans to guarantee quality of teachers and the teaching learning process. In this paper,
an optimization Evolution model for academic performance of the faculty’s in technical institutions based on
teaching activity series of qualitative reports is presented. We have proposed a Fuzzy Expert System for
evaluating teachers overall performance based on fuzzy logic techniques under uncertain facts in the decision
making process. A suitable fuzzy inference mechanism and associated rules are been discussed. It introduces the
principles behind fuzzy logic and illustrates how these principles could be applied by educators to evaluating
faculty’s performance. This model will help to write the Annual Confidential Reports of all the employees of an
organization.
Keywords - Fuzzy logic, R&D, Faculty, Member Function.
I. INTRODUCTION
In developing countries, like India and other
countries, higher education is seen as an essential
means for creation and development of resources and
for improving the life of people to whom it has to
serve. A highly reliable and effective performance
evaluation rule is essential in decision making
environments. There is increased consensus that
highly qualified, quality, and effective teachers and
teaching is necessary to improve the academic
performance of the students and there is growing an
interest in identifying individual teacher’s impact on
student’s achievement and also improvement of
image of the educational institutes. Federal
definitions suggest every faculty to assess themselves
regularly to meet this requirement. Though students
gain on standardized achievements is one important
aspect of teaching ability, it is not only the
comprehensive and robust view of teacher
effectiveness.
In this paper we propose an optimization
model and an interactive online Faculty Performance
Appraisal System provides faculty’s with meaningful
appraisals that encourage professional learning and
growth. The process is designed to foster teacher
development and identify opportunities for additional
support where required [6].To assess the performance
of individual faculty in the institutions by integrating
planning and review in the areas viz., Feedback from
students, Teachers self appraisal, Assessment by
peers, and Results of University exams by providing
a structure Online Interactive Interface that possesses
potential related assessment data of Faculty in
educational institutions. By helping teachers achieve
their full potential, the performance appraisal process
represents one element of achieving high levels of
student performance.
Conventional evaluation systems are
representatives of structured systems that employ
quantifiable and non quantifiable measures of
evaluation. It is often difficult to quantify
performance dimensions. For example, “teaching”
may be an important part of the appraisal. However,
how exactly does one measure “teaching”. Academic
administrators often face such issues when trying to
evaluate a staff’s performance.
And also staff has to spend a lot of time in evaluating
each faculty performance manually. This is not only a
RESEARCH ARTICLE OPEN ACCESS

time consuming process but may sometimes lead to
errors in calculations. These two problems may
increase the number of persons who involve in the
process. Fuzzy approach can be effectively utilized to
handle imprecision and uncertainty [7]. This
approach to performance appraisal allows the
organization to exercise professional judgment in
evaluating its employees. In real problems,
evaluation techniques engage in handling cases like
subjectivity, fuzziness and imprecise information.
Application of the fuzzy set theory in evaluation
systems can improve evaluation results [1]. Several
researchers have tried to solve this problem through
the analytical hierarchy process (AHP) [2], for
example in personnel selection [3] and shipping
performance evaluation [4], whereby evaluation is
done by aggregating all the fuzzy sets.
This paper has seven sections. The next
section gives a survey of Fuzzy Logic System in
evolution of faculty performance. Section 3 describes
the optimal Architectural Model for Faculty
Performance Assessment. Section 4 describes the
Proposed Method for Faculty Performance
Evaluation. Section 5 describes the Fuzzy Expert
System for Faculty Performance Evaluation. Section
6 describes the experimental result of proposed rule
based Fuzzy Expert System. We conclude paper with
Section 7.
II. SURVEY OF FUZZY METHODS IN
ASSESSMENT OF FACULTY
PERFORMANCE
While fuzzy logic techniques have earned their place
in a variety of field ranging from engineering to
financial sector, to medicine, few efforts have been
made to test the potential usefulness of these methods
in the modeling academic performance evaluation.
This section discusses the literature survey about the
past and current research application of fuzzy logic. It
discusses about the academic achievement of student
and teacher, prediction model and academic
performance evaluation fuzzy logic approaches in
academic performance evaluation.
(A) Modeling Academic Performance Evaluation
Using Soft Computing Techniques: A Fuzzy Logic
Approach.
Ramjeet Singh Yadav et al., (2011) [9], proposed a
Fuzzy Expert System (FES) for student academic
performance evaluation using fuzzy logic techniques.
A suitable fuzzy inference mechanism and associated
rule has been discussed. It introduces the principles
behind fuzzy logic and illustrates how these
principles could be applied by educators to evaluate
student academic performance. Several approaches
using fuzzy logic techniques have been proposed to
provide a practical method for evaluating student
academic performance and comparing the results
(performance) with existing statistical method.
(B) Evaluation of Teacher’s Performance
Evaluation Using Fuzzy Logic Techniques.
Sirigiri Pavani et al., (2012) [8], proposed a
method to deal with the evaluation of teacher’s
academic performance evaluation using fuzzy logic
techniques of Fuzzification of Semester Examination
Results and Performance Value.
(C) Soft Computing Model for Academic
Performance of Teachers Using Fuzzy
Logic
O.K. Chaudhari et al., (2012) [10] proposed
a Fuzzy Expert System for evaluating teachers
overall performance based on fuzzy logic techniques
under “uncertain facts” in the decision making
process. A suitable fuzzy inference mechanism and
associated rule has been discussed. It introduces the
principles behind fuzzy logic and illustrates how
these principles could be applied by educators to
evaluate teachers’ performance. This model will help
to write the Annual Confidential Reports of all the
employees of an organization.
(D) An Evaluation of Students Performance in
Oral Presentation Using Fuzzy Approach
Wan Suhan Wan Daud et al., (2011) [11],
proposed a method for evaluating student’s academic
performance using fuzzy logic approach. They
pointed that the evaluation of students’ performance
is a process of making judgment on a student based
on several elements such as examinations,
assignment, test, quiz, research work and so on. They
have used the following methodology for evaluating
students’ performance.
(E) Fuzzy Logic Based Evaluation of
Performance of Students in Colleges
Mamatha S. Upadhya (2012) [12], proposed
a method for evaluation of students’ performance
based on fuzzy logic. This system is dealt with the
range of possible values for the input and output
variables determined. These (in language of fuzzy set
theory) are the membership function (input variables
vs. the degree of membership function) used to map
the real world measurement values to the fuzzy
values. Values of the input variables are considered
in term of percentage.
III. ARCHITECTURAL MODAL FOR
FACULTY PERFORMANCE
ASSESSMENT
The evaluation of teaching activity can be
defined as the systematic evaluation of teaching
performance according to the professional role and
contribution required to reach the objectives of the
course taking into consideration the institutional
context [13]. Therefore, teaching activity implies the
planning and management of teaching, the

deployment of teaching methods, learning and
evaluation activities, and finally the revision and
improvement of the procedures carried out. A multi-
criteria analysis in ranking the quality of teaching
using fuzzy rule was proposed in [14].
To put the existing teachers on track, it is
very necessary to evaluate their performance, may be
in quarterly, in semester or annually, depending upon
the resources in academic institutes. University or the
institutions of higher education do not have uniform
standard method or computerized solution for
evaluating teachers’ performance that covers all
factors affecting directly or indirectly the quality of
university or the institutes. Hence the fuzzy logic
model is introduced to evaluate the teachers overall
performance through his or her involvement in the
various sub activity involved in the institute. The
proposed Optimized architectural model of Faculty’s
Assessment system in Fig.1.
Fig.1 Architectural Modal for Faculty Performance
Assessment
Feed Back from Students: Keeping a record of
faculty activities and insights from seeking feedback
on faculty teaching and units is an essential aid to
your reflection, particularly over time as memory
inevitably dims. Such records help you in going
through the cycle of clarifying your teaching goals,
identifying strengths and weaknesses in achieving
these goals, narrowing down any areas for
improvement, devising courses of action for
improvement, and reflecting on these changes as they
are put into practice.
Teacher Self Appraisal: Teacher self appraisal is a
mechanism for improving teaching and learning. We
all agree that teachers’ professional competence and
conscientiousness are the keys to the delivery of
quality education in educational institutions. In a
well-designed staff appraisal system, the instruments
and procedures can constitute valuable professional
development for teachers and enable the college
management to assess teachers’ performance. The
teacher appraisal system assists in recognizing and
encouraging good performance, identifying areas for
development, and improving overall performance of
teachers.
Assessment by Peers: Peer review of academic
practice is commonplace in educational institutes. It
is a well accepted source of information for
development and assessment in the realms of
personal quality and professional quality.
Unfortunately, there is a tendency to think that peer
review of teaching works in the same way as peer
review of personal quality and professional quality. It
is this misapprehension of peer review in teaching
that associates it so strongly with the assessment of
teaching. There is an assessment function for peer
review in teaching, of course, with Heads of
departments being a major source of information for
tenure and promotion at the institutions and
elsewhere. Its greater virtue however is in the
development of teaching. Peer review involves
informed and formative exchanges between
colleagues on every aspect of what they do to help
learning to occur. Peer reviewers work together to
improve the way they work individually with and for
students. Under ideal conditions they do this
collaboratively over a period of time.
Results and University Exams: Writing effective
and efficient exams is a crucial component of the
teaching and learning process. Exams are a common
approach to assess student learning and the results are
useful in a variety of ways. Most often, results are
used to provide students feedback on what they
learned or evaluate the instructional effectiveness of a
course.
IV. PROPOSED METHOD FOR
FACULTY PERFORMANCE
EVALUATION
One of the drawbacks of the conventional
faculty evaluation methods in Fig.2 (a), is the lack of
information behind the evaluation methods that have
been used and what criteria for the 'final result'. To
do so, a fuzzy approach has been used to perform the
proposed method of faculty performance evaluation.
It is important to point out that the aim of the
proposed method is not to replace the current
traditional method of evaluation, instead it will
strengthen the present system by providing additional
information to be used for decision making by the
user through online system.Fig.2(b) shows the
proposed method fuzzy Expert System of faculty
performance evaluation. This system for storage of
data has been planned to use the Oracle Database and
all the user interfaces has been designed using the
JSP technologies. It takes care of different modules

and their associated functionalities and reports which
are produced as per the applicable strategies.
(a) Process of Traditional Evolution
(b) Proposed method using Fuzzy logic
Fig.2 Proposed Method Fuzzy Expert System for
Faculty Performance Evaluation
Evolution parameters: Based on the above
discussion, Fuzzy Expert System considers the
various elements of performance measures of
teachers with different modules as shown in Table 1.
The fuzzy numbers are:
5- Excellent 4-Very good 3-Good
2-Average 1-Poor
Table 1 Parameters of the evaluation model
Table 1(a) Student Feedback Parameters
Representation
of Fuzzy
Variables
Fuzzy Variables
F1
Feedback parameters
(Grade/rank 1-5)
F11 Quality of teaching
F111 Pace of subject
F112
F113
F114
F115
F116
F117
F118
Used good examples and
illustrations
Motivated to attend the
classes
Used blackboard
efficiently
Used audio visual aids
like OHP, LCD, etc.
Group
discussion/seminar
helped in learning
Stimulated my interest in
the subject
Audibility and clarity of
speech
F12 Factors in learning
F121
F122
Lectures contributed to
my learning
Defined learning
objectives for each period
F13 Assessment of learning
F131
F132
F133
F134
F135
Teacher's feedback on my
assignments was useful
Questions given in exams
are from the topics taught
Problem sets helped me
learn
I can apply the subject
concepts
Answer papers are
evaluated fairly
F14 Mentoring/counseling
F141
F142
Teacher was
approachable outside the
classes
Teacher was sympathetic
to academic/personal
problems
Table 1(b) Teachers Self Appraisal Parameters
Representation of
Fuzzy Variables
Fuzzy Variables
F2 Teachers Self Appraisal
F21 Teaching
F211
F212
F213
F214
F215
F216
F217
Preparation of course
plan
Preparation of class
notes
Syllabus coverage
Quality and quantity of
illustrations & examples
Satisfaction level about
your communication
abilities
Use of teaching aids like
models /animations/
photographs, etc.,

F218
F219
Frequency of using
OHP/LCD to match with
lesson requirements
Average attendance
percentage of students
Innovation methods of
teaching, if used(give
details separately)
F22 Assessment of learning
F221
F222
F223
F224
F225
F225
Level of student
response to your
questioning during class
Average marks of class
in the slip tests at the end
of the class or class tests
at the end of the
unit(specify the number
of tests conducted)
Laying down learning
objectives for each topic
Organizing group
discussions as per plan
Conducting student
seminars as per approved
schedule
Timely evaluation of
assignments
F23 Mentoring and
counseling
F231
F232
F233
Number of students met
Time spent with each
student
Quality of outcome
F24 Administrative
Functions
F241
F242
Supportive role to HOD
(attendance monitoring,
upkeep of laboratories &
manuals, addition of
books to library,
departmental files,
timetables etc.)
Supportive role to
Principal (anti-ragging,
dress code, discipline,
industrial/educational
tours etc.)
F25 R&D functions
F251
F252
F253
F254
F255
Papers published in
international/National
journals
Participated in seminars
/symposia
Guidance of student
projects
Current research projects
and progress during the
period
F256
Seminars/symposia
organized as
convener/co-
coordinator/secretary
Current Published
Textbooks
Table 1(c) Assessment by Peers Parameters
Representation
of Fuzzy
Variables
Fuzzy Variables
F3 Assessment by Peers
F31 Provisional Qualities
F311
F312
F313
F314
F315
F316
F317
F318
F319
F31A
F31B
F31C
F31D
F31E
Breadth & depth of
knowledge of his/her
subject
Updating habits of subject
knowledge
Knowledge in related areas
Comprehension skills
Communication abilities
Oral
Written
Application to work
Lerner centered pedagogical
skills
Academic planning and
implementation
Preparation of class notes
Ability to guide student
project work
Administrative support
R&D functions
F32 Personal qualities
F321
F322
F323
F324
F325
F326
F327
F328
F329
Punctuality
Devotion of duty
Integrity
Capacity to work as a team
member
Interpersonal relations
Emotional balance
Intellectual honesty
Mentoring/Counseling
capability
Fairness in students'
evaluation
Table 1(d) Results and University Exams Parameters
Representation of
Fuzzy Variables
Fuzzy Variables
F4 Results and University
Exams
F41 Subject-1

F411
F412
F413
F414
% Passes
% 1st Classes(>59%
<70% only)
% Distinctions(>69%
only)
%Class average marks
F42 Subject-2
F421
F422
F423
F424
% Passes
% 1st Classes(>59%
<70% only)
% Distinctions(>69%
only)
F43 Subject-3
F431
F432
F433
F434
% Passes
% 1st Classes(>59%
<70% only)
% Distinctions(>69%
only)
% Passes Calculation:
 5- Excellent, if >90% pass
 4-Very good, if >80% & <70% passes
 3-Good, if >70% & <60% passes
 2-Average, if >60% & <50% passes
 1-Poor, if <50% pass
% 1st Classes (>59% <70% only) Calculation:
 5- Excellent, if >60% & <70% 1st classes,
 4-Very good, if >40% & < 60% 1st classes
 3-Good, if >20% & < 40% 1st classes
 2-Average, if >0% & < 20% 1st classes
 1-Poor, if 0 % 1st class
% Distinctions (>69% only) Calculation:
 5- Excellent, if >75% distinctions in class
strength,
 4-Very good, if >55% & < 75% distinctions in
class strength
 3-Good, if >35% & < 55% distinctions in class
strength
 2-Average, if >0% & <35% distinctions in class
strength
 1-Poor, if 0 % distinctions in class strength
%Class average marks Calculation:
 5- Excellent, if >60% Class Average
 4-Very good, if >55% & < 60% Class Average
 3-Good, if > 45% & < 55% Class Average
 2-Average, if >40% & < 45% Class Average
 1-Poor, if, <40% Class Average
V. FUZZY EXPERT SYSTEM FOR
FACULTY PERFORMANCE
EVALUATION
Performance Evaluation of faculty with
Fuzzy Expert System comprised with three steps:
1. Identification of crisp value.
2. Fuzzification of input value.
3. Determination of application rules and inference
method.
4. Fuzzy output overall performance value and
Defuzzification of performance value.
1 Crisp Value (Data)
Teachers self-appraisal forms are filled in by
respective teachers on the above elements with sub
activity which then recommended by the Head of the
Department and head of the institution with due
verification. The Crisp data is tabulated from these
forms (Table 7).
2 Fuzzification (Fuzzy Input Value)
The input variables (elements/parameters)
are then divided into linguistic variables 5-
Excellent,4-Very good, 3-Good, 2-Average, and 1-
Poor. Membership functions are then formed
assigning the proper range to respective linguistic
variables. In this paper we have used the trapezoidal
membership function for converting the crisp set into
fuzzy set as in eqn. (1).
Feed Back from Students
Table 2. Student’s feedback
Year/Branch
/Section (1)
Subject
Taught
(2)
Student
Feedbac
k(3)
Overall
Student
Feedback
(%)(4)
B1 S1
F11
F1=Avg.
points of
Col. 3
F12
F13
F14
Range for linguistic variables of the Students
Feedback (F1) is shown in Table 3.

Table 3. Students’ feedback in terms of linguistic
variables
Membership Function of the input variable Students
Feedback (F1) is shown in Fig. 3.
Fig. 3. Membership function of input variable F1
The remaining Membership Functions of the
input variables like Teacher Self Appraisal (F2),
Assessment by Peers (F3), and Results and
University Exams (F4) are calculated same as the
calculation of member function of F1. These results
are produced using Fuzzy tool in mat-lab.
3 Fuzzy Rule and Inference Mechanism
The rules determine input and output
membership functions that will be used in inference
process. These rules are linguistics and are entitled
“IF-THEN” rules. From the discussion with the
academic experts some rules are formulated from
their practical and past experiences. In this study
since the number of input variables are more, more
number of rules are framed to justify important
variables of the Results and University Exam and
the academic institute. Some of the rules for fuzzy
system as shown below:
1. If (SFB(F1) is Poor) and (TSA(F2) is Poor) and
(AP(F3) is Poor) and (RUE(F4) is Poor) then
(Assessment Results(O) is Poor)
2. If (SFB(F1) is Average) and (TSA(F2) is Poor)
and (AP(F3) is Poor) and (RUE(F4) is Poor) then
(Assessment Results(O) is Poor)
3. If (SFB(F1) is Very Good) and (TSA(F2) is
Poor) and (AP(F3) is Poor) and (RUE(F4) is
Poor) then (Assessment Results(O) is Poor)
4. If (SFB(F1) is Poor) and (TSA(F2) is Average)
and (AP(F3) is Average) and (RUE(F4) is
Average) then (Assessment Results(O) is
Average)
Average) and (AP(F3) is Average) and
(RUE(F4) is Average) then (Assessment
Results(O) is Average)
6. If (SFB(F1) is Average) and (TSA(F2) is
8. If (SFB(F1) is Good) and (TSA(F2) is Good) and
(AP(F3) is Good) and (RUE(F4) is Good) then
(Assessment Results(O) is Good)
9. If (SFB(F1) is Poor) and (TSA(F2) is Good) and
(AP(F3) is Good) and (RUE(F4) is Good) then
(Assessment Results(O) is Good)
Good) and (AP(F3) is Good) and (RUE(F4) is
Good) then (Assessment Results(O) is Good)
11. If (SFB(F1) is Excellent) and (TSA(F2) is Good)
and (AP(F3) is Good) and (RUE(F4) is Good)
then (Assessment Results(O) is Good)
12. If (SFB(F1) is Very Good) and (TSA(F2) is Very
Good) and (AP(F3) is Very Good) and
(RUE(F4) is Very Good) then (Assessment
Results(O) is Very Good)
13. If (SFB(F1) is Poor) and (TSA(F2) is Very
14. If (SFB(F1) is Good) and (TSA(F2) is Very
15. If (SFB(F1) is Excellent) and (TSA(F2) is Very
16. If (SFB(F1) is Excellent) and (TSA(F2) is
Excellent) and (AP(F3) is Excellent) and
(RUE(F4) is Excellent) then (Assessment
Results(O) is Excellent)
Results(O) is Excellent)
18. If (SFB(F1) is Average) and (TSA(F2) is
19. If (SFB(F1) is Poor) and (TSA(F2) is Very
(RUE(F4) is Poor) then (Assessment Results(O)
is Poor)
Student
Feedba
ck
Po
or
Avg
Go
od
V.Go
od
Exc
elle
nt
F1
[1
1.2
6
1.6
26
2]
[1.5
2.08
2.46
3
2.8]
[2.
3
2.8
3.2
3.5
]
[3
3.596
4.01
4.3]
[3.8
4.46
4.9
5]

20. If (SFB(F1) is Average) and (TSA(F2) is Poor)
and (AP(F3) is Poor) and (RUE(F4) is Average)
then (Assessment Results(O) is Average)
21. If (SFB(F1) is Average) and (TSA(F2) is Good)
and (AP(F3) is Good) and (RUE(F4) is Good)
then (Assessment Results(O) is Good)
Results(O) is Good)
Poor) and (AP(F3) is Poor) and (RUE(F4) is
Excellent) then (Assessment Results(O) is Good)
24. If (SFB(F1) is Excellent) and (TSA(F2) is Very
(RUE(F4) is Good) then (Assessment Results(O)
is Very Good)
25. If (SFB(F1) is Excellent) and (TSA(F2) is
Average) and (AP(F3) is Good) and (RUE(F4) is
Excellent) then (Assessment Results(O) is Very
Good)
F1= Student Feedback(SFB).
F2=Teachers Self Appraisal(TSA)
F3= Assessment by Peers (AP)
F4= Results and University Exam (RUE)
O= Assessment Results(AR)
4 Fuzzy Output and Defuzzification
The output variable is the overall
performance of the teacher, which has five linguistic
Variables. The degree of membership functions is
given by equation (2).
This expression determines an output
membership function value for each active rule.
When one rule is active, an AND operation is applied
between inputs. The fuzzy linguistic variables of
output variable are shown in Table 4.
Table 4. Teachers’ Overall Performance in terms of
Linguistic Variable
Faculty’s
Overall
Perfor
mance
Po
or
Ave
rage
Go
od
Ve
ry
Go
od
Exce
llent
O
[1
1.4
1
1.7
9
2.1
]
[1.6
2.24
2.66
2.7]
[2.
3
2.9
44
3.3
7
3.4
]
[3
3.7
7
4.1
4.2
]
[3.8
4.48
4.86
5]
Membership Function of the output variable Overall
Performance of a Faculty (O) is shown in Fig. 4
Fig. 4. Membership function of teachers overall
performance
Calculation of Performance Value:
After completing the fuzzy decision process,
the fuzzy number obtained must be converted to a
crisp value. This process is known as Defuzzification.
In this paper, a centre gravity of area technique was
applied is called Centroid technique, which is one of
the most common methods for converting from fuzzy
number to crisp values [15]. The centroid
defuzzification technique can be expressed for the
calculation of crisp value:
x*= (3)
where x*
is the defuzzified output, µi(x) is the
aggregated membership function and x is the output
variable. Using this method the observation results
was computationally easier and got accurate results.
Rule viewer of the proposed fuzzy expert
system for the evaluation of overall faculty’s
performance is shown in Fig. 5.
Fig. 5. Rule Viewer of fuzzy expert system
Surface viewer of proposed fizzy expert
system for academic performance evaluation is
shown in Fig. 6.

Fig. 6. Surface Viewer of Fuzzy Expert System
VI. RESULTS AND DISCUSSIONS
Experiments are carried out using Mat-lab
fuzzy toolbox on Windows XP platform.
The proposed Fuzzy Export model was
studied and tested with 50 faculties data obtained in
the year 2013 from repudiated engineering college,
Vignan’s Institute of Information Technology,
Visakhapatnam. Table 5 shows the 10 faculty’s data
of both the traditional and fuzzy score. From the
input data the output variable overall performance of
teacher is determined by traditional method (based on
statistical averaging method) and also by using the
fuzzy model developed in the study. Last two
columns of Table 5 shows the values of teachers
overall performance by traditional method and Fuzzy
Expert System respectively.
Table 5. Teachers overall performance (crisp and
fuzzy)
S.
No
.
Input Variables(F)
Output (O)
F1
SFB
F2
TSA
F3
AP
F4
RUE
Tradi
tional
Fuzzy
01 4 4 2 5 3.75 4.06
3
02 4 5 5 5 4.75 4.78
03 5 5 1 4 3.75 4.46
04 4 5 2 5 4 4.37
05 5 3 1 4 3.25 3.92
06 5 4 1 5 3.75 4.46
07 3 4 3 4 3.5 3.57
08 1 3 3 5 3 3.66
09 2 4 3 3 3 3.16
10 4 5 5 3 4.25 4.41
We observed the difference in the direct
value and the values determined by using fuzzy
model. Using traditional method in 5th
record has the
value 3.25 i.e. 3 indicates the grade as good, but in
the case of fuzzy is 3.92 i.e 4 graded as very good. So
the overall performance of a faculty determined by
fuzzy model is more realistic than the direct values.
VII. CONCLUSIONS
Teachers’ regular assessment is suggested to
maintain quality in higher education. There is a vast
potential of the applications of fuzzy expert system in
teachers’ assessment. Expert system technology
using Fuzzy Logic is very interesting for quantitative
and qualitative facts evaluation. In this paper a model
of Fuzzy Expert System is proposed to evaluate
teachers overall performance on the basis of various
related activities. The qualitative variables are
mapped into numeric results by implementing the
fuzzy expert system model through various input
examples and provided a basis to use the system for
further decision making. In this way the teaching
staff is encouraged to reflect on quality, adequacy,
satisfaction, efficiency and innovation in teaching in
the technical academic institutions.
ACKNOWLEDGEMENTS
I thank to my management of Vignan’s
Institute of Information Technology one of the
reputed Engineering College located in
Visakhapatnam, Andhra Pradesh, India for providing
the data to develop the model of evaluation of
teachers overall performance.
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